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opencc-by-4.0Dec 2023View details →

Data and Scripts for Publication: Information Bottleneck in Peptide Conformation Determination by X-ray Absorption Spectroscopy

Data and scripts for the publication titled Information Bottleneck in Peptide Conformation Determination by X-ray Absorption Spectroscopy. Article available at <a href="https://doi.org/10.1088/2399-6528/ad1f73">10.1088/2399-6528/ad1f73</a>

opencc-zeroDec 2020View details →

x-ray tube

The presented object is a Crookes cold cathode X-ray tube. Time and place of creation: ca. 1920, Germany Jagiellonian University Museum Collegium Maius Inventory number: 5144; 1341/V https://muzea.malopolska.pl/en/objects-list/2766 Source: Objaverse 1.0 / Sketchfab

dryad36/100

Evidence for phonon hardening in laser excited gold using x-ray diffraction at a hard x-ray free electron laser

Studies of laser-heated materials on femtosecond timescales have shown that the interatomic potential can be perturbed at sufficiently high laser intensities. For gold, it has been postulated to undergo a strong stiffening leading to an increase of the phonon energies, known as phonon hardening. Despite efforts to investigate this behavior, only measurements at low absorbed energy density have been performed, for which the interpretation of the experimental data remains ambiguous. By using in situ single-shot x-ray diffraction at a hard x-ray free electron laser, the evolution of diffraction line intensities of laser-excited Au to a higher energy density provides evidence for phonon-hardening.

opencc-zeroJan 2024View details →

opencc-by-4.0Feb 2024View details →

Data and geometries for "Understanding X-ray absorption in liquid water using triple excitations in multilevel coupled cluster theory"

Geometries and raw and processed data for the paper "Understanding X-ray absorption in liquid water using triple excitations in multilevel coupled cluster theory" This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Program (grant agreement no. 101020016 and 860553), the Research Council of Norway through FRINATEK (project no. 275506), the Swedish Research Council (grant agreement no. 2021-04521), the Independent Research Fund Denmark--Natural Sciences, DFF-RP2 (grant no. 7014-00258B) Computing resources from UNINETT Sigma2—the National Infrastructure for High Performance Computing and Data Storage in Norway (project no. NN2962k), from DeIC—Danish Infrastructure Cooperation (grant no. DeiC-DTU-N3-2023027), and from the Swiss National Supercomputing Centre  (project ID uzh1).

X-ray powder diffraction patterns of Bi2-xSbxTe3 (x=0, 0.2, 0.5, 1.5, 1.8, 2.0) nanoparticles

The data set contains X-ray powder diffraction patterns of Bi2-xSbxTe3 (x=0, 0.2, 0.5, 1.5, 1.8, 2.0) nanoparticles, synthesized utilizing microwave-assisted heating. Diffraction data were collected at room temperature using a benchtop Rigaku MiniFlex 600 diffractometer with Bragg-Brentano θ-2θ geometry. An X-ray tube with a copper anode (Cu Kα radiation, λ =1.5418 Å), operated at U = 40 kV and I = 15 mA, was used as a source. Bi2Te3 and Sb2Te3 are isostructural and crystallize in a rhombohedral crystal system with the space group R-3m (No. 166).

opencc-by-4.0Sep 2023View details →

The Tarantula--Revealed by X-rays (T-ReX)

Supplemental data products for the journal article "The Tarantula--Revealed by X-rays (T-ReX)," to be published in the Astrophysical Journal Supplements in 2024. Please see README.txt for a description of the contents of this archive.

X-ray diffraction dataset for the complex between CNPase and nanobody 5E

Raw diffraction data for the crystal structure between mouse CNPase catalytic domain and anti-CNPase nanobody 5E.

X-ray diffraction data for CNPase bound to nanobody 8C

X-ray diffraction dataset for complex between mouse CNPase catalytic domain and anti-CNPase nanobody 8C

X-ray diffraction data for the complex between CNPase and nanobody 7E

X-ray diffraction dataset for the complex between mouse CNPase catalytic domain and nanobody 7E.

X-ray diffraction data for the complex between CNPase and nanobody 10E

X-ray diffraction dataset for the complex between mouse CNPase catalytic domain and anti-CNPase nanobody 10E

PENGWIN Task 2: Pelvic Fragment Segmentation on Synthetic X-ray Images

The <a href="https://pengwin.grand-challenge.org/">PENGWIN segmentation challenge</a> is designed to advance the development of automated pelvic fracture segmentation techniques in both 3D CT scans (Task 1) and 2D X-ray images (Task 2), aiming to enhance their accuarcy and robustness. The full 3D dataset comprises CT scans from 150 patients scheduled for pelvic reduction surgery, collected from multiple institutions using a variety of scanning devices. This dataset represents a diverse range of patient cohorts and fracture types. Ground-truth segmentations for sacrum and hipbone fragments have been semi-automatically annotated and subsequently validated by medical experts, and are available <a href="https://doi.org/10.5281/zenodo.10927452">here</a>. From this 3D data, we have generated high-quality, realistic X-ray images and corresponding 2D labels from the CT data using <a href="https://github.com/arcadelab/deepdrr">DeepDRR</a>, incorporating a range of virtual C-arm camera positions and surgical tools. This dataset contains the training set for fragment segmentation in synthetic X-ray (task 2). The training set is derived from 100 CTs, with 500 images each, for a total of 50,000 training images and segmentations. The C-arm geometry is randomly sampled for each CT within reasonable parameters for a full-size C-arm. The virtual patient is assumed to be in a head-first supine position. Imaging centers are randomly sampled within 50 mm of a fragment, ensuring good visibility. Viewing directions are sampled uniformly on the sphere within 45 degrees of vertical. Half of the images (IDs XXX_0250 - XXX_0500) contain up to 10 simulated K-wires and/or orthopaedic screws oriented randomly in the field of view. The input images are raw intensity images without any windowing or normalization applied. It is standard practice to first apply the negative log transformation and then window each image appropriately for feeding into a model. See the included augmentation pipeline in `pengwin_utils.py` for one approach. For viewing raw images, the <a href="https://imagej.net/software/fiji/">FIJI</a> image viewer is a viable option, but it is recommended to use the included visualization functions in `pengwin_utilities.py` to first apply CLAHE normalization and save to a universally readable PNG (see example usage below). Because X-ray images feature overlapping segmentation maks, the segmentations have been encoded as multi-label uint32 images, where each pixel should be treated as a binary vector with bits 1 - 10 for SA fragments, 11 - 20 for LI, and 21 - 30 for RI. Thus, the raw segmentation files are not viewable with standard image viewing software. `pengwin_utilities.py` includes functions for converting to and from this format and for visualizing masks overlaid onto the original image (see below). To use the utilities, first install dependencies with `pip install -r requirement.txt`. Then, to visualize an image with its segmentation, you can do the following (assuming the training set has been downloaded and unzipped in the same folder): <pre><code>import pengwin_utils from PIL import Image image_path = "train/input/images/x-ray/001_0000.tif" seg_path = "train/output/images/x-ray/001_0000.tif" # load image and masks image = pengwin_utils.load_image(image_path) # raw intensity image masks, category_ids, fragment_ids = pengwin_utils.load_masks(seg_path) # save visualization of image and masks # applies CLAHE normalization to the raw intensity image before overlaying segmentations. vis_image = pengwin_utils.visualize_sample(image, masks, category_ids, fragment_ids) vis_path = "vis_image.png" Image.fromarray(vis_image).save(vis_path) print(f"Wrote visualization to {vis_path}") # Obtain predicted masks, category_ids, and fragment_ids # Category IDs are {"SA": 1, "LI": 2, "RI": 3} # Fragment IDs are the integer labels from label_{category}.nii.gz, with 1 corresponding to the main fragment. pred_masks, pred_category_ids, pred_fragment_ids = masks, category_ids, fragment_ids # replace with your model # save the predicted masks for upload to the challenge # Note: cv2 does not work with uint32 images. It is recommended to use PIL or imageio.v3 pred_seg = pengwin_utils.masks_to_seg(pred_masks, pred_category_ids, pred_fragment_ids) pred_seg_path = "pred/train/output/images/x-ray/001_0000.tif" # ensure dir exists! Image.fromarray(pred_seg).save(pred_seg_path) print(f"Wrote segmentation to {pred_seg_path}")</code></pre> The `pengwin_utils.Dataset` class is provided as an example of a Pytorch dataset, with strong domain randomization included to facilitate sim-to-real performance, but it is recommended to write your own as needed.

Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM

This respository corresponds to the following paper: Wankowicz et al. Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM (2024). These are the qFit models. MTZ and deposited models cna be downloaded from the PDB.

Cryo-EM and X-ray crystallography ligands represented as 3D voxel grids for training deep learning models

Ligand datasets used to train and evaluate the models studied in "Ligand Identification using Deep Learning" by Karolczak, J. et al. The blobs_full.tar.gz and cryoem_blobs.zip files contain compressed 3D numpy arrays (*.npz) of all the ligand blobs extracted from X-ray and cryo-EM PDB deposits prior to quality filtering. The npz file names correspond to the PDB ID, chain, residue number, and ligand name of the extracted blob. The cmb_data.csv file contains the tabular data used to train the CheckMyBlob model. The X-ray data were later divided into training and testing subsets according to the xray_train.csv and xray_holdout.csv files, respectively. The ligand_mapping.csv file contains the mapping from ligand IDs to ligand group names. Finally, the cryoem_qscores.csv file contains Q-scores that were used to filter cryo-EM ligands.

Raw X-ray data, CSD Communication 2024, deposiiton numbers 2349939 and 2349940

Raw data for two datasets: SC-XRD measurements for a single crystal and a non-merohedral twin of CuCl2(pyr)2 complex. Collected with Mo radiation.

opencc-by-4.0Nov 2024View details →

Cryo soft X-ray tomography of 5 microns thick slab extracted from C. elegans midbody region

Samples of C. elegans larvae of strain DM8005 were high pressure frozen using the waffle method. Multiple roughly 5 micron thick slabs spanning an L2 larva were extracted using the Serial Lift-Out technique, and placed on rectangular mesh grids. Soft X-ray tomography was performed on individual slabs using the SXT-100 soft x-ray microscope at SiriusXT. The data reveals label free internal structure of fully hydrated C. elegans tissue, annotating the worm volume and adding context to cryo electron tomography data.  SXT data was acquired from -50° to +53° in 1° steps and reconstructed using Weighted Back Projection. A 32 nm objective zone plate was used. Data pixel size is 29 nm.

opencc-by-4.0Nov 2024View details →

Cryo soft X-ray tomography of 5 microns thick slab extracted from C. elegans anterior region

opencc-by-4.0Oct 2024View details →

X-Rays micro-tomography of 54 Egyptian predynastic ceramic vessels from the MAN (Musée d'Archéologie Nationale-Domaine national du château de Saint-Germain-en-Laye, France) made in the frame of the project TECHNOPREGYPT 2021/43/P/HS3/03262. STACK DATA

54 complete pots and sherds from the collection of the Musée d’Archéologie Nationale-Domaine national du château de Saint-Germain-en-Laye, France, were scanned with X-Rays microtomography (CT-Scan) to carry out the technological analysis of the samples. The aim was to be able to see the internal structure of the ceramic vessels to reconstruct the manufacturing process. The scans are part of the project TECHNOPREGYPT  Ceramic technology and the socio-political environment of Predynastic Egypt  2021/43/P/HS3/03262 directed by dr Jade BAJEOT.  <a title="TECHNOPREGYPT website" href="https://technopregypt.iksio.pan.pl/index.php/about-the-project">TECHNOPREGYPT website</a>   Funding This research was led at the Institute of Mediterranean and Oriental Cultures, Polish Academy of Sciences and was co-funded by the Polish National Science Centre and the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 945339. For the purpose of Open Access, the author has applied a CC-BY public copyright license to any Author Accepted Manuscript (AAM) version arising from this submission.

opencc-by-sa-4.0Oct 2024View details →

AdverX-Ray: Ensuring X-Ray Integrity Through Frequency-Sensitive Adversarial VAEs

Open the record for dataset details and reuse information.

dryad36/100

X-ray diffraction data, crystallographic information file, infrared spectra, and LA-ICP-MS depthprofiles of davemaoite

Calcium silicate perovskite, CaSiO3, is arguably the most geochemically important phase in the lower mantle, because it concentrates elements that are incompatible in the upper mantle, including the heat-generating elements thorium and uranium, which have half-lives longer than the geologic history of Earth. We report CaSiO3-perovskite as an approved mineral (IMA2020-12a) with the name davemaoite. The natural specimen of davemaoite proves the existence of compositional heterogeneity within the lower mantle. Our observations indicate that davemaoite also hosts potassium in addition to uranium and thorium in its structure. Hence, the regional and global abundances of davemaoite influence the heat budget of the deep mantle, where the mineral is thermodynamically stable.

opencc-zeroNov 2021View details →